People have become increasingly dependent on email for their daily communication. Email is popular because it is fast, easy, and has little incremental cost. Unfortunately, these advantages of email are also exploited by marketers who regularly send out large amounts of unsolicited junk email (also referred to as “spam”). Spam messages are a nuisance for email users. They clog people's email box, waste system resources, often promote distasteful subjects, and sometimes sponsor outright scams.
There have been efforts to block spam using spam-blocking software in a collaborative environment where users contribute to a common spam knowledge base. For privacy and efficiency reasons, the spam-blocking software generally identifies spam messages by using a signature generated based on the content of the message. A relatively straightforward scheme to generate a signature is to first remove leading and trailing blank lines then compute a checksum on the remaining message body. However, spam senders (also referred to as “spammers”) have been able to get around this scheme by embedding variations—often as random strings—in the messages so that the messages sent are not identical and generate different signatures.
Another spam-blocking mechanism is to remove words that are not found in the dictionary as well as leading and trailing blank lines, and then compute the checksum on the remaining message body. However, spammers have been able to circumvent this scheme by adding random dictionary words in the text. These superfluous words are sometimes added as white text on a white background, so that they are invisible to the readers but nevertheless confusing to the spam-blocking software.
The existing spam-blocking mechanisms have their limitations. Once the spammers learn how the signatures for the messages are generated, they can alter their message generation software to overcome the blocking mechanism. It would be desirable to have a way to identify messages that cannot be easily overcome even if the identification scheme is known. It would also be useful if any antidote to the identification scheme were expensive to implement or would incur significant runtime costs.